Journey to Bristol: my first ever Data Study Group hosted by the Turing
A week ago, I attended a turing data study group that was held in Bristol. The Turing committe said that it was their first time hosting a study group outside London, and the University of Bristol had taken this opportunity.
On Monday morning, we saw a group of pinked shirts who had been our greatest helpers throughout the whole period. After the welcome talk was given, the six challenge PIs introduced their research, data, and agendas for the particpants to tackle for the rest of the week. As my phd project is about air pollution exposure, I chose the city council’s challenge, Get Bristol Moving - Tackling air pollution in Bristol City Centre - to test who well I understood pollution during my degree, but also to apply my knowledge to another city that wasn’t familiar at all!
- To read about the challenge, click this link.
What was I doing?
I met 8 participants and a facilitator to work on this challenge. Each of us had different experience and skills, so we allocated our group into four sub-projects (air quality, transport, weather, and various modelling), of which I was grouped in the air quality project.
Data collection
The air quality dataset of Bristol was available in the Bristol Opendata Portal, which was achived and published by the city council. We collected live and historic air quality continous data (1998-2019) from 16 monitoring stations.
Bristol has been monitored nitro oxides (NOx, that include NO and NO2) from a single site since January 1998, and has expanded it to multiple stations around the city centre. This might be because the NOx level is time-variant, meaning that rush hour congestion can cause negative health outcomes in a long-term (Zhang and Batterman, 2013).
At present, the city has 7 Stations (location with bold text) that monitors NOx, NO2, NO, and PM10 (see Figure 1 or ctrl + click). Each station has its own intention of monitoring: Colston avenue (detects wose case exposre in city centre), AURN St Pauls (detects background exposure in residential area), Brislington depot (Freight), Fishponds road (residential and shopping), Parson street school (residential area + school zone), Wells road (continous traffic in and out of city centre).
[Table 1. General information of pollution monitoring sites in Bristol. The seven stations (with bolded text) are the currently measuring pollution data]
| Location | Long | Lat | Date_Start | Date_End | Total period (for terminated stations) |
|---|---|---|---|---|---|
| AQ Mesh Temple Way | -2.56208 | 51.47528 | 2019-01-01 | 2019-04-23 | approx. 3 months |
| AURN St Pauls | -2.55996 | 51.44175 | 2006-06-15 | 2019-08-05 | |
| Bath Road | -2.60496 | 51.43267 | 2005-10-29 | 2013-01-04 | approx. 7 years |
| Brislington Depot | -2.59626 | 51.45543 | 2001-01-01 | 2019-08-05 | |
| Cheltenham Road Station Road | -2.58448 | 51.44888 | 2008-06-25 | 2011-01-01 | approx 2.5 years |
| Colston Avenue | -2.56374 | 51.42786 | 2018-03-11 | 2019-08-05 | |
| Fishponds Road | -2.68878 | 51.48999 | 2009-03-13 | 2019-08-05 | |
| IKEA M32 | -2.56272 | 51.45779 | 1998-01-10 | 2000-12-06 | approx 3 years |
| Newfoundland Road Police Station | -2.58225 | 51.46067 | 2005-01-01 | 2015-12-31 | 10 years |
| Parson Street School | -2.57138 | 51.44254 | 2002-01-02 | 2019-08-05 | |
| Rupert Street | -2.58454 | 51.46283 | 2003-01-01 | 2015-12-31 | approx. 12 years |
| Shiner’s Garage | -2.59273 | 51.46894 | 2004-06-24 | 2013-01-04 | approx 8.5 years |
| Temple Meads Station | -2.53523 | 51.47804 | 2003-02-01 | 2003-10-27 | approx 9 months |
| Temple Way | -2.58399 | 51.45795 | 2017-04-01 | 2019-08-05 | |
| Trailer Portway P&R | -2.59665 | 51.45527 | 2004-03-01 | 2009-03-01 | approx 5 years |
| Wells Road A37 Airport Road Junction | -2.58399 | 51.45795 | 2003-05-23 | 2019-08-05 |
Data Pre-processing
File download and rename columns
My colleague is a data scientist in a retail consultancy, and one of this jobs was to understand the huge chunk of profile from customers. From his experience, he thought the first thing to do was data cleaning and renaming columns. He downloaded the files named Air Quality Data Continuous.csv using tidyverse, then joined local weather data gathered from a station just outside Bristol city centre. He finally saved the huge data frame with his favourite, feather package, which is compatible with R and Python.
And guess what? It just took me 2 seconds to load a 1.1 million rows across 41 variables!! This was why it was called feather!
Sys.setlocale("LC_ALL","English")
library(tidyverse)
library(tidyquant)
library(leaflet)
library(feather)
aq <- read_feather("air_and_weather.feather") %>%
arrange(datetime)Take a glance at the raw data as below! You can see there are over 40 different variables measure from different stations by an hourly basis. Unfortunately, most of the variables were missing, but luckily our main pollutants nitro oxides (NOx, NO2, NO) were monitored.
# A tibble: 1,146,616 x 20
`Date Time` NOx NO2 NO PM10 SiteID NVPM10 VPM10 NVPM2.5 PM2.5 VPM2.5 CO O3 SO2 Temperature RH Location geo_point_2d
<dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <chr> <chr>
1 2017-11-27 15:00:00 23.5 16.3 4.65 7.1 452 5.8 1.3 3.8 5.6 1.8 NA 64.8 NA NA NA AURN St… 51.46282815…
2 2017-11-27 18:00:00 23.7 18.9 3.12 3.7 452 3 0.7 1.9 3.8 1.9 NA 62.3 NA NA NA AURN St… 51.46282815…
3 2017-11-27 22:00:00 26.7 23.1 2.34 12.6 452 10 2.6 3.5 3.9 0.4 NA 48.7 NA NA NA AURN St… 51.46282815…
4 2017-11-28 06:00:00 26.7 22.5 2.76 6 452 6 0 2.5 3.1 0.6 NA 50.9 NA NA NA AURN St… 51.46282815…
5 2017-11-28 10:00:00 40.5 26.8 8.92 9.7 452 9.3 0.4 5.1 5.5 0.4 NA 46.7 NA NA NA AURN St… 51.46282815…
6 2017-11-28 20:00:00 63.8 48.7 9.88 14.9 452 15.2 -0.3 10.6 10.7 0.1 NA 20.6 NA NA NA AURN St… 51.46282815…
7 2017-11-28 21:00:00 50.0 39.2 7.05 10.1 452 8.6 1.5 5.2 4.4 -0.8 NA 27.8 NA NA NA AURN St… 51.46282815…
8 2017-11-29 05:00:00 19.0 17.1 1.26 6.6 452 6.7 -0.1 2.4 2.2 -0.2 NA 46.4 NA NA NA AURN St… 51.46282815…
9 2018-04-23 10:00:00 18.9 12.5 4.16 13.5 452 9.1 4.4 2.6 6.1 3.5 NA 87.1 NA NA NA AURN St… 51.46282815…
10 2018-04-23 15:00:00 14.7 10.2 2.95 7.9 452 5.4 2.5 2.3 3.5 1.2 NA 87.1 NA NA NA AURN St… 51.46282815…
# … with 1,146,606 more rows, and 2 more variables: DateStart <dttm>, DateEnd <dttm>
Map locations
Having had a look at the data, I wanted to explore the locations of each station. In R, you can use unique to filter out the unnecessay data and leave the stations’ info.
# Check the location_names
aq %>%
select(location_name, lon, lat) %>%
unique() -> location_names_alltime
# Overall location_names 16
location_names_alltime %>%
leaflet() %>%
addTiles() %>%
addMarkers(~lon, ~lat, label = ~as.character(location_name)) As you can see, 10 stations are within 2 miles from the city centre, while the other 6 were situated outside. The members from the city council mentioned that station installation and removal is subjected to the contracts from AQ instrument companies. Despite a lack of stations, the local government installed over 100 diffusion tubes across the whole city.
Results
Temporal Exploration of NO2 in Bristol City
- In general, all stations (except AQ Mesh Temple way site,) have a daily and sesonal oscilation of NO2.
- However, there was a variation by stations. In [Table ], the averaged NO2 was the highest on Rupert street (city centre) at 93.1µg/m3, followed by Colston Avenue and Temple Meads station at 65.7, 63.2 respectively.
- Parson Street school which have been monitored since 2002 had an average of 47.7µg/m3, but the hourly concentration of NO2 had exceed the legal limit for 154,189 times. Although it had declined in the recent years, the exceedance counts are very much of a concern.
[Table Mean NO2 and counts of exceedance of 200µg/m3 by each station]
| Station Name | Mean NO2 | Number of Exceedance |
|---|---|---|
| Trailer Portway P&R | 24.4 | 43,824 |
| Brislington Depot | 26.3 | 162,971 |
| AURN St Pauls | 28.3 | 115,139 |
| Cheltenham Rd | 34.5 | 22,071 |
| Bath Road | 38.7 | 62,990 |
| Temple Way | 40.4 | 20,553 |
| Fishponds Road | 41.1 | 90,945 |
| Shiner’s Garage | 42 | 74,787 |
| Wells Rd | 44 | 142,001 |
| Parson Street School | 47.7 | 154,189 |
| Newfoundland Rd | 54.4 | 96,407 |
| IKEA M32 | 61.2 | 25,464 |
| Temple Meads Station | 63.2 | 6,446 |
| Colston Avenue | 65.7 | 12,284 |
| Rupert Street | 93.1 | 113,951 |
Monthly aggreated boxplots by hours
- Overall, the averaged NO2 tends to soar rapidly between 7am and 10am, which roughly peaks at twice the amount than the concentration at 5 am. It remains the concentration until 6pm, then gradually decreases late at night.
- For example, Parson street school has the lowest NO2 concentration just above 20µg/m3 at 4am, then rose up to 54µg/m3 at 10am.
Monthly aggregated boxplots by days of week after 2018
- The boxplot used the the monthly average of NO2 at 7 stations in 2018-2019.
- Overall, the weekday concentration was higher than that of weekends. Amongst all stations, colston avenue (city centre) had the highest amount of NO2 that ranged 50-70µg/m3 during weekdays and falled 60 µg/m3 on Saturdays, then decreased futher on Sundays at around 50µg/m3. Parson Street school just managed to go below the national NO2 limit of 40µg/m3 during weekdays.
- School Holiday
- Overall, while there is a small difference in between holidays and non-holidays (5µg/m3), it has a distiction by locations.
- Colston Avenue a consistantly high concentration of NO2 above 50 regardless of holidays, but the concentration varied during holidays. The same happened to Temple Way.
- During term time, children who were commuting to schools near Parson street primary school or passing Fishponds road might have experienced a high level of NO2 since the concentration ranges up to 80µg/m3.
Summary and Future Work
This study firstly explored the temporal features of pollutants (NOx), then examined Parson Street School as a case study.
- Our initial finding was that most stations had a daily and seasonal oscillation of NO2 throughout the whole period ranging from less than 20µg/m3 to over 1000µg/m3 based on hourly measurement.
- Looking in to an averaged hourly NO2, there was a clear trough around 20µg/m3 at 3-5am but peaked at around 60µg/m3 after 10am.
- However, the concentration was always higher in the city centre sites (Parson Street School, Rupert Street). Although the average NO2 levels of Parson street schooler were just below the UK legal limit (40µg/m3), the children might have consistently encountered instantaneous NO2 exposure during drop-off and pick up times.
Our second finding was that the NO2 concentration between holidays and non-holidays (school holidays and bank holidays 2018-2019) was on average less than 5µg/m3, however varied by locations.
- Limitation and future work
- This study attempted to relate NO2 to holidays to speculate the effect traffic levels, however failed to find the association due to the lack of data and time. Future work should therefore consider to link vehicle data to understand the relationship between Bristol’s vehicle flow and air pollution and how it affects school students.
- Careful investigation of traffic should be done to associate with AQ
- Personal exposure monitoring studies can help measuring the actual harmness or cleanness in Bristol
What did I learn?
Needless to say, I learned to work as a team. Understanding each of our perspectives and ideas is important, however, we need to be mindful of our words and behaviours when we have to tackle a common goal. Sometimes one can be a leader of the project (because they know this theme better or they are good at allocating jobs), while the other can make this experience as to learn how to code, or use the time to discuss things together. If one wants to patrionise their knowledge without any justification, the team would end up in a disaster.
Secondly, Bristol’s air pollution is problematic however can be alleviated by understanding various urban aspects. Here, urban aspects can mean traffic signals, urban form, or road width. Bristol is a very hilly city and is rich of traffic signals. We know that traffic signals is meant to control traffic flow and protect pedestrians, but we didn’t consider how acceleration and deceleration can generate harmful pollutants. Moreover, if a vehicle stops in the middle of a hill and starts again, more pollutants are emitted and affect pedestrians. I woudn’t conclude saying we should carve all the hills, but it is worthwhile to understand why pollution is more concentrated in such areas in Bristol.
